Fake News Detection using Voting Technique

Description: This project aims to combat the spread of fake news by proposing a novel system for detection. The approach involves two key steps: preprocessing and prediction. In preprocessing, text blocks are broken down into sentences, converted to lowercase, and lemmatized to create a clean and consistent dataset. The prediction step utilizes machine learning algorithms, including Logistic Regression, Decision Trees, Naive Bayes, XGBoost, and Stochastic Gradient Descent. A unique voting technique combines the strengths of individual models, mitigates weaknesses, and improves overall accuracy. The GitHub repository includes code for the proposed system, comprehensive evaluations, and insights into leveraging machine learning and ensemble techniques for effective fake news detection.